## File: plot_isolation_forest.py

package info (click to toggle)
scikit-learn 0.18-5
 `123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869` ``````""" ========================================== IsolationForest example ========================================== An example using IsolationForest for anomaly detection. The IsolationForest 'isolates' observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node. This path length, averaged over a forest of such random trees, is a measure of abnormality and our decision function. Random partitioning produces noticeable shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies. .. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest." Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import IsolationForest rng = np.random.RandomState(42) # Generate train data X = 0.3 * rng.randn(100, 2) X_train = np.r_[X + 2, X - 2] # Generate some regular novel observations X = 0.3 * rng.randn(20, 2) X_test = np.r_[X + 2, X - 2] # Generate some abnormal novel observations X_outliers = rng.uniform(low=-4, high=4, size=(20, 2)) # fit the model clf = IsolationForest(max_samples=100, random_state=rng) clf.fit(X_train) y_pred_train = clf.predict(X_train) y_pred_test = clf.predict(X_test) y_pred_outliers = clf.predict(X_outliers) # plot the line, the samples, and the nearest vectors to the plane xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50)) Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.title("IsolationForest") plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r) b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white') b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green') c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red') plt.axis('tight') plt.xlim((-5, 5)) plt.ylim((-5, 5)) plt.legend([b1, b2, c], ["training observations", "new regular observations", "new abnormal observations"], loc="upper left") plt.show() ``````